import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
import numpy as np
import math
from math import sqrt


class PositionalEmbedding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEmbedding, self).__init__()
        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]


class TokenEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(TokenEmbedding, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_in', nonlinearity='leaky_relu')

    def forward(self, x):
        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
        return x


class FixedEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(FixedEmbedding, self).__init__()

        w = torch.zeros(c_in, d_model).float()
        w.require_grad = False

        position = torch.arange(0, c_in).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        w[:, 0::2] = torch.sin(position * div_term)
        w[:, 1::2] = torch.cos(position * div_term)

        self.emb = nn.Embedding(c_in, d_model)
        self.emb.weight = nn.Parameter(w, requires_grad=False)

    def forward(self, x):
        return self.emb(x).detach()


class TemporalEmbedding(nn.Module):
    def __init__(self, d_model, embed_type='fixed', freq='h'):
        super(TemporalEmbedding, self).__init__()

        minute_size = 4
        hour_size = 24
        weekday_size = 7
        day_size = 32
        month_size = 13

        Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
        if freq == 't':
            self.minute_embed = Embed(minute_size, d_model)
        self.hour_embed = Embed(hour_size, d_model)
        self.weekday_embed = Embed(weekday_size, d_model)
        self.day_embed = Embed(day_size, d_model)
        self.month_embed = Embed(month_size, d_model)

    def forward(self, x):
        x = x.long()
        minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
            self, 'minute_embed') else 0.
        hour_x = self.hour_embed(x[:, :, 3])
        weekday_x = self.weekday_embed(x[:, :, 2])
        day_x = self.day_embed(x[:, :, 1])
        month_x = self.month_embed(x[:, :, 0])

        return hour_x + weekday_x + day_x + month_x + minute_x


class TimeFeatureEmbedding(nn.Module):
    def __init__(self, d_model, embed_type='timeF', freq='h'):
        super(TimeFeatureEmbedding, self).__init__()

        freq_map = {'h': 4, 't': 5, 's': 6,
                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
        d_inp = freq_map[freq]
        self.embed = nn.Linear(d_inp, d_model, bias=False)

    def forward(self, x):
        return self.embed(x)


class DataEmbedding(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding, self).__init__()

        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
            d_model=d_model, embed_type=embed_type, freq=freq)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        if x_mark is None:
            x = self.value_embedding(x) + self.position_embedding(x)
        else:
            x = self.value_embedding(
                x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
        return self.dropout(x)


class DataEmbedding_inverted(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding_inverted, self).__init__()
        self.value_embedding = nn.Linear(c_in, d_model)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        x = x.permute(0, 2, 1)
        # x: [Batch Variate Time]
        if x_mark is None:
            x = self.value_embedding(x)
        else:
            x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
        # x: [Batch Variate d_model]
        return self.dropout(x)


class DataEmbedding_wo_pos(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding_wo_pos, self).__init__()

        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
            d_model=d_model, embed_type=embed_type, freq=freq)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        if x_mark is None:
            x = self.value_embedding(x)
        else:
            x = self.value_embedding(x) + self.temporal_embedding(x_mark)
        return self.dropout(x)


class PatchEmbedding(nn.Module):
    def __init__(self, d_model, patch_len, stride, padding, dropout):
        super(PatchEmbedding, self).__init__()
        # Patching
        self.patch_len = patch_len
        self.stride = stride
        self.padding_patch_layer = nn.ReplicationPad1d((0, padding))

        # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)

        # Positional embedding
        self.position_embedding = PositionalEmbedding(d_model)

        # Residual dropout
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # do patching
        n_vars = x.shape[1]
        x = self.padding_patch_layer(x)
        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
        # Input encoding
        x = self.value_embedding(x) + self.position_embedding(x)
        return self.dropout(x), n_vars

class AutoCorrelation(nn.Module):
    """
    AutoCorrelation Mechanism with the following two phases:
    (1) period-based dependencies discovery
    (2) time delay aggregation
    This block can replace the self-attention family mechanism seamlessly.
    """

    def __init__(self, mask_flag=True, factor=1, scale=None, attention_dropout=0.1, output_attention=False):
        super(AutoCorrelation, self).__init__()
        self.factor = factor
        self.scale = scale
        self.mask_flag = mask_flag
        self.output_attention = output_attention
        self.dropout = nn.Dropout(attention_dropout)

    def time_delay_agg_training(self, values, corr):
        """
        SpeedUp version of Autocorrelation (a batch-normalization style design)
        This is for the training phase.
        """
        head = values.shape[1]
        channel = values.shape[2]
        length = values.shape[3]
        # find top k
        top_k = int(self.factor * math.log(length))
        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
        index = torch.topk(torch.mean(mean_value, dim=0), top_k, dim=-1)[1]
        weights = torch.stack([mean_value[:, index[i]] for i in range(top_k)], dim=-1)
        # update corr
        tmp_corr = torch.softmax(weights, dim=-1)
        # aggregation
        tmp_values = values
        delays_agg = torch.zeros_like(values).float()
        for i in range(top_k):
            pattern = torch.roll(tmp_values, -int(index[i]), -1)
            delays_agg = delays_agg + pattern * \
                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
        return delays_agg

    def time_delay_agg_inference(self, values, corr):
        """
        SpeedUp version of Autocorrelation (a batch-normalization style design)
        This is for the inference phase.
        """
        batch = values.shape[0]
        head = values.shape[1]
        channel = values.shape[2]
        length = values.shape[3]
        # index init
        init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1).to(values.device)
        # find top k
        top_k = int(self.factor * math.log(length))
        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
        weights, delay = torch.topk(mean_value, top_k, dim=-1)
        # update corr
        tmp_corr = torch.softmax(weights, dim=-1)
        # aggregation
        tmp_values = values.repeat(1, 1, 1, 2)
        delays_agg = torch.zeros_like(values).float()
        for i in range(top_k):
            tmp_delay = init_index + delay[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length)
            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
            delays_agg = delays_agg + pattern * \
                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
        return delays_agg

    def time_delay_agg_full(self, values, corr):
        """
        Standard version of Autocorrelation
        """
        batch = values.shape[0]
        head = values.shape[1]
        channel = values.shape[2]
        length = values.shape[3]
        # index init
        init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1).to(values.device)
        # find top k
        top_k = int(self.factor * math.log(length))
        weights, delay = torch.topk(corr, top_k, dim=-1)
        # update corr
        tmp_corr = torch.softmax(weights, dim=-1)
        # aggregation
        tmp_values = values.repeat(1, 1, 1, 2)
        delays_agg = torch.zeros_like(values).float()
        for i in range(top_k):
            tmp_delay = init_index + delay[..., i].unsqueeze(-1)
            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
            delays_agg = delays_agg + pattern * (tmp_corr[..., i].unsqueeze(-1))
        return delays_agg

    def forward(self, queries, keys, values, attn_mask):
        B, L, H, E = queries.shape
        _, S, _, D = values.shape
        if L > S:
            zeros = torch.zeros_like(queries[:, :(L - S), :]).float()
            values = torch.cat([values, zeros], dim=1)
            keys = torch.cat([keys, zeros], dim=1)
        else:
            values = values[:, :L, :, :]
            keys = keys[:, :L, :, :]

        # period-based dependencies
        q_fft = torch.fft.rfft(queries.permute(0, 2, 3, 1).contiguous(), dim=-1)
        k_fft = torch.fft.rfft(keys.permute(0, 2, 3, 1).contiguous(), dim=-1)
        res = q_fft * torch.conj(k_fft)
        corr = torch.fft.irfft(res, dim=-1)

        # time delay agg
        if self.training:
            V = self.time_delay_agg_training(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)
        else:
            V = self.time_delay_agg_inference(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)

        if self.output_attention:
            return (V.contiguous(), corr.permute(0, 3, 1, 2))
        else:
            return (V.contiguous(), None)


class AutoCorrelationLayer(nn.Module):
    def __init__(self, correlation, d_model, n_heads, d_keys=None,
                 d_values=None):
        super(AutoCorrelationLayer, self).__init__()

        d_keys = d_keys or (d_model // n_heads)
        d_values = d_values or (d_model // n_heads)

        self.inner_correlation = correlation
        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
        self.value_projection = nn.Linear(d_model, d_values * n_heads)
        self.out_projection = nn.Linear(d_values * n_heads, d_model)
        self.n_heads = n_heads

    def forward(self, queries, keys, values, attn_mask):
        B, L, _ = queries.shape
        _, S, _ = keys.shape
        H = self.n_heads

        queries = self.query_projection(queries).view(B, L, H, -1)
        keys = self.key_projection(keys).view(B, S, H, -1)
        values = self.value_projection(values).view(B, S, H, -1)

        out, attn = self.inner_correlation(
            queries,
            keys,
            values,
            attn_mask
        )
        out = out.view(B, L, -1)

        return self.out_projection(out), attn

class my_Layernorm(nn.Module):
    """
    Special designed layernorm for the seasonal part
    """

    def __init__(self, channels):
        super(my_Layernorm, self).__init__()
        self.layernorm = nn.LayerNorm(channels)

    def forward(self, x):
        x_hat = self.layernorm(x)
        bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)
        return x_hat - bias


class moving_avg(nn.Module):
    """
    Moving average block to highlight the trend of time series
    """

    def __init__(self, kernel_size, stride):
        super(moving_avg, self).__init__()
        self.kernel_size = kernel_size
        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)

    def forward(self, x):
        # padding on the both ends of time series
        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
        x = torch.cat([front, x, end], dim=1)
        x = self.avg(x.permute(0, 2, 1))
        x = x.permute(0, 2, 1)
        return x


class series_decomp(nn.Module):
    """
    Series decomposition block
    """

    def __init__(self, kernel_size):
        super(series_decomp, self).__init__()
        self.moving_avg = moving_avg(kernel_size, stride=1)

    def forward(self, x):
        moving_mean = self.moving_avg(x)
        res = x - moving_mean
        return res, moving_mean


class series_decomp_multi(nn.Module):
    """
    Multiple Series decomposition block from FEDformer
    """

    def __init__(self, kernel_size):
        super(series_decomp_multi, self).__init__()
        self.kernel_size = kernel_size
        self.series_decomp = [series_decomp(kernel) for kernel in kernel_size]

    def forward(self, x):
        moving_mean = []
        res = []
        for func in self.series_decomp:
            sea, moving_avg = func(x)
            moving_mean.append(moving_avg)
            res.append(sea)

        sea = sum(res) / len(res)
        moving_mean = sum(moving_mean) / len(moving_mean)
        return sea, moving_mean


class EncoderLayer(nn.Module):
    """
    Autoformer encoder layer with the progressive decomposition architecture
    """

    def __init__(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu"):
        super(EncoderLayer, self).__init__()
        d_ff = d_ff or 4 * d_model
        self.attention = attention
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False)
        self.decomp1 = series_decomp(moving_avg)
        self.decomp2 = series_decomp(moving_avg)
        self.dropout = nn.Dropout(dropout)
        self.activation = F.relu if activation == "relu" else F.gelu

    def forward(self, x, attn_mask=None):
        new_x, attn = self.attention(
            x, x, x,
            attn_mask=attn_mask
        )
        x = x + self.dropout(new_x)
        x, _ = self.decomp1(x)
        y = x
        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
        y = self.dropout(self.conv2(y).transpose(-1, 1))
        res, _ = self.decomp2(x + y)
        return res, attn


class Encoder(nn.Module):
    """
    Autoformer encoder
    """

    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
        super(Encoder, self).__init__()
        self.attn_layers = nn.ModuleList(attn_layers)
        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
        self.norm = norm_layer

    def forward(self, x, attn_mask=None):
        attns = []
        if self.conv_layers is not None:
            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
                x, attn = attn_layer(x, attn_mask=attn_mask)
                x = conv_layer(x)
                attns.append(attn)
            x, attn = self.attn_layers[-1](x)
            attns.append(attn)
        else:
            for attn_layer in self.attn_layers:
                x, attn = attn_layer(x, attn_mask=attn_mask)
                attns.append(attn)

        if self.norm is not None:
            x = self.norm(x)

        return x, attns


class DecoderLayer(nn.Module):
    """
    Autoformer decoder layer with the progressive decomposition architecture
    """

    def __init__(self, self_attention, cross_attention, d_model, c_out, d_ff=None,
                 moving_avg=25, dropout=0.1, activation="relu"):
        super(DecoderLayer, self).__init__()
        d_ff = d_ff or 4 * d_model
        self.self_attention = self_attention
        self.cross_attention = cross_attention
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False)
        self.decomp1 = series_decomp(moving_avg)
        self.decomp2 = series_decomp(moving_avg)
        self.decomp3 = series_decomp(moving_avg)
        self.dropout = nn.Dropout(dropout)
        self.projection = nn.Conv1d(in_channels=d_model, out_channels=c_out, kernel_size=3, stride=1, padding=1,
                                    padding_mode='circular', bias=False)
        self.activation = F.relu if activation == "relu" else F.gelu

    def forward(self, x, cross, x_mask=None, cross_mask=None):
        x = x + self.dropout(self.self_attention(
            x, x, x,
            attn_mask=x_mask
        )[0])
        x, trend1 = self.decomp1(x)
        x = x + self.dropout(self.cross_attention(
            x, cross, cross,
            attn_mask=cross_mask
        )[0])
        x, trend2 = self.decomp2(x)
        y = x
        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
        y = self.dropout(self.conv2(y).transpose(-1, 1))
        x, trend3 = self.decomp3(x + y)

        residual_trend = trend1 + trend2 + trend3
        residual_trend = self.projection(residual_trend.permute(0, 2, 1)).transpose(1, 2)
        return x, residual_trend


class Decoder(nn.Module):
    """
    Autoformer encoder
    """

    def __init__(self, layers, norm_layer=None, projection=None):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList(layers)
        self.norm = norm_layer
        self.projection = projection

    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):
        for layer in self.layers:
            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
            trend = trend + residual_trend

        if self.norm is not None:
            x = self.norm(x)

        if self.projection is not None:
            x = self.projection(x)
        return x, trend


class Autoformer(nn.Module):
    """
    Autoformer is the first method to achieve the series-wise connection,
    with inherent O(LlogL) complexity
    Paper link: https://openreview.net/pdf?id=I55UqU-M11y
    """

    def __init__(self, task_name='short_term_forecast',seq_len=96, label_len=48, pred_len=96, enc_in=7, dec_in=7, c_out=1, e_layers=2, d_layers=1, n_heads=8,factor=3,
                 d_model=16, d_ff=32, des='Exp', expand=2, d_conv=4, top_k=5, embed='timeF',freq='h', dropout=0.1,num_kernels=6,
                 moving_avg=25,channel_independence=1, decomp_method='moving_avg', use_norm=1,
                 version='fourier', mode_select='random', modes=32, activation='gelu',seasonal_patterns='Monthly',
                 inverse=False, mask_rate=0.25, anomaly_ratio=0.25,output_attention=False,down_sampling_layers=0, down_sampling_window=1, down_sampling_method=None,
                seg_len=48, num_workers=0, itr=1, train_epochs=100, batch_size=32, patience=3, learning_rate=0.0001, loss='MSE',
                lradj='type1', use_amp=False, use_gpu=True, gpu=0, use_multi_gpu=False, devices='0,1,2,3', p_hidden_dims=[128, 128],
                p_hidden_layers=2, use_dtw=False, augmentation_ratio=0, seed=2, jitter=False, scaling=False, permutation=False,
                randompermutation=False, magwarp=False, timewarp=False, windowslice=False, windowwarp=False, rotation=False,
                spawner=False, dtwwarp=False, shapedtwwarp=False, wdba=False, discdtw=False, discsdtw=False, extra_tag='',
                 **kwargs):
        super(Autoformer, self).__init__()
        self.task_name = task_name
        self.seq_len = seq_len
        self.label_len = label_len
        self.pred_len = pred_len
        self.output_attention = output_attention

        # Decomp
        kernel_size = moving_avg
        self.decomp = series_decomp(kernel_size)

        # Embedding
        self.enc_embedding = DataEmbedding_wo_pos(enc_in, d_model, embed, freq, dropout)
        # Encoder
        self.encoder = Encoder(
            [
                EncoderLayer(
                    AutoCorrelationLayer(
                        AutoCorrelation(False, factor, attention_dropout=dropout,
                                        output_attention=output_attention),
                        d_model, n_heads),
                    d_model,
                    d_ff,
                    moving_avg=moving_avg,
                    dropout=dropout,
                    activation=activation
                ) for l in range(e_layers)
            ],
            norm_layer=my_Layernorm(d_model)
        )
        # Decoder
        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
            self.dec_embedding = DataEmbedding_wo_pos(dec_in, d_model, embed, freq,dropout)
            self.decoder = Decoder(
                [
                    DecoderLayer(
                        AutoCorrelationLayer(
                            AutoCorrelation(True, factor, attention_dropout=dropout,
                                            output_attention=False),
                            d_model, n_heads),
                        AutoCorrelationLayer(
                            AutoCorrelation(False, factor, attention_dropout=dropout,
                                            output_attention=False),
                            d_model, n_heads),
                        d_model,
                        c_out,
                        d_ff,
                        moving_avg=moving_avg,
                        dropout=dropout,
                        activation=activation,
                    )
                    for l in range(d_layers)
                ],
                norm_layer=my_Layernorm(d_model),
                projection=nn.Linear(d_model, c_out, bias=True)
            )

        self.projection_final = nn.Linear(pred_len*enc_in, pred_len*c_out, bias=True)
    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
        # decomp init
        mean = torch.mean(x_enc, dim=1).unsqueeze(
            1).repeat(1, self.pred_len, 1)
        zeros = torch.zeros([x_dec.shape[0], self.pred_len,
                             x_dec.shape[2]], device=x_enc.device)
        seasonal_init, trend_init = self.decomp(x_enc)
        # decoder input
        if self.label_len == 0:
            trend_init = trend_init
            seasonal_init = seasonal_init
        else:
            trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1)
            seasonal_init = torch.cat([seasonal_init[:, -self.label_len:, :], zeros], dim=1)

        # enc
        enc_out = self.enc_embedding(x_enc, x_mark_enc)
        enc_out, attns = self.encoder(enc_out, attn_mask=None)
        # dec
        dec_out = self.dec_embedding(seasonal_init, x_mark_dec)
        seasonal_part, trend_part = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None,trend=trend_init)
        # final
        dec_out = trend_part + seasonal_part
        dec_out=dec_out[:, -self.pred_len:, :]
        dec_out=self.projection_final(dec_out.view(dec_out.shape[0], -1))
        return dec_out

from pytorch_forecasting.models import BaseModel
from typing import Dict

class AutoFormerNetModel(BaseModel):
    def __init__(self,seq_len=24, label_len=0, pred_len=1, enc_in=7, dec_in=7, c_out=1, e_layers=2, d_layers=1, factor=3,
                 d_model=16, d_ff=32, des='Exp', itr=1, top_k=5,embed='timeF',freq='h', dropout=0.1,num_kernels=6, **kwargs):
        # saves arguments in signature to `.hparams` attribute, mandatory call - do not skip this
        self.save_hyperparameters()
        # pass additional arguments to BaseModel.__init__, mandatory call - do not skip this
        super().__init__(**kwargs)
        self.network = Autoformer(
            seq_len=seq_len, label_len=label_len, pred_len=pred_len, enc_in=enc_in, dec_in=dec_in, c_out=c_out, e_layers=e_layers, d_layers=d_layers, factor=factor,
            d_model=d_model, d_ff=d_ff, des=des, itr=itr, top_k=top_k, embed=embed, freq=freq, dropout=dropout, num_kernels=num_kernels
        )
        self.label_len=label_len

    # 修改，锂电池预测
    def forward(self, x: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:

        x_enc = x["encoder_cont"][:,:,:-1]  # torch.Size([100, 10, 9])
        x_dec = torch.cat([x["encoder_cont"][:, -self.label_len:, :-1], x["decoder_cont"][:,:,:-1]],
                        dim=1)  # torch.Size([100, 11, 9])
        # 输出
        prediction = self.network(x_enc=x_enc,x_mark_enc=None,x_dec=x_dec,x_mark_dec=None)
        # 输出rescale， rescale predictions into target space
        prediction = self.transform_output(prediction, target_scale=x["target_scale"])

        # 返回一个字典，包含输出结果（prediction）
        return self.to_network_output(prediction=prediction)


if __name__=='__main__':
    N,L,C=100,96,15
    label_len = 16
    c_out = 1
    pred_len=16
    x_enc=torch.ones((N,L,C))
    x_mark_enc=torch.ones((N, L, 4))
    x_dec = torch.ones((N, pred_len, C))
    x_mark_dec=torch.ones((N, pred_len, 4))
    model=Autoformer(seq_len=L, enc_in=C, dec_in=C, label_len = label_len, pred_len=pred_len, c_out=1)              # pred_len 被限制了
    out=model(x_enc=x_enc, x_mark_enc=x_mark_enc, x_dec=x_dec, x_mark_dec=x_mark_dec)
    print(out.shape)